To overcome this problem we add additional constraints to the grammar to ensure that all parses the grammar generates belong to the same tree class.

one way to do this is to include the start state of a transition as an element of the terminal, e.g., we extend the terminals to include the left-hand side of the productions they are emitted from, e.g., and the sequence of terminals becomes: {P 4 } $.


The advantage of these models, depicted in Figure 1, is their ability to encode features that capture properties of a segment as a whole, which is not possible in an HMM model.

these features can encode similarities between two sequence segments of arbitrary lengths, which can be very useful in tasks such as APS.


The third feature type is specific to Semi-Markov models.

these features can encode properties of a whole segment or similarities between two sequence segments of arbitrary lengths.


Since T needs to be computed for all values of t + − M ≤ t − < t + , we need to compute O(|x|M ) many values, each of which requires an optimization over M possible values.

storage requirements are O(|x|M ), whereas the computation scales with O(|x|M 2 ).


Note that these bilingual features will only fire on those supervised or unsupervised sentences for which we have an English translation.

they will usually be unavailable on the test set.


These are shown to increase either the likelihood of the training data, or a lower bound on that likelihood.

abney defines a function K that is an upper bound on the negative log-likelihood, and shows his bootstrapping algorithms locally minimize K. We now present a generalization of abney's K function and relate it to another semi-supervised learning technique, entropy regularization (Brand, 1999;Grandvalet and Bengio, 2005;Jiao et al., 2006).


As we will see, some confidence measures correlate better than others.

measures that are more peaked around the one-best prediction of the parser, as in Viterbi re-estimation, perform well.


The use of a detailed grammatical formalism such as CCG has the advantage that it allows a system to handle quite complex semantic effects, such as coordination or scoping phenomena.

it allows us to leverage the considerable body of work on semantics within these formalisms, for example see Carpenter (1997).


We add features in the model which track the occurrences of each of these four new combinators.

we have four new features in the definition of f; each feature tracks the number of times one of the combinators is used in a CCG parse.


a nonparametric Bayesian model of syntactic tree structures based on Dirichlet processes.

an HDP-PCFG is defined to have an infinite number of symbols; the Dirichlet process (DP) prior penalizes the use of more symbols than are supported by the training data.


The central idea behind variational inference is to approximate this intractable posterior with a tractable approximation.

we want to find the best distribution q * as defined by where Q is a tractable subset of distributions.


Since this subtraction affects large counts more than small counts, there is a rich-get-richer effect: rules that have already have large counts will be preferred.

consider a set of rules with the same left-hand side.


Gazetteer Match (gaz m) This feature represents the matching with a gazetteer entry by using "B", "I", and "O" tags.

this is the gazetteer version of wp m below.


Gazetteer Category Label (gaz c) This feature represents the matching with a gazetteer entry and its category by using "B-X", "I-X", and "O" tags, where X is one of "PER", "LOC", "ORG", and "MISC".

this is the gazetteer version of wp c below.


The first is that the sub-trees enumerated in the tree kernel computation are context-free.

each sub-tree enumerated in the tree kernel computation does not consider the context information outside the sub-tree.


As an alternative to the feature-based methods, the kernel-based methods (Haussler, 1999) have been proposed to implicitly explore various features in a high dimensional space by employing a kernel to calculate the similarity between two objects directly.

the kernel-based methods could be very effective at reducing the burden of feature engineering for structured objects in NLP researches, e.g.


Zhang et al (2006) systematically explored seven different tree spans, including the Shortest Path-enclosed Tree (SPT) and a Context-Sensitive Path-enclosed Tree 1 (CSPT), and found that SPT per formed best.


In this paper, a simple method is applied by regarding the "predicatelinked" category as the default category.

those entity pairs, which do not belong to the four well defined and easily detected categories (i.e.


For comparison, we use the same setting as Zhang et al (2006) by applying a 5-fold cross-validation on a subset of the 2004 data, containing 348 documents and 4400 relation instances.

all the reported performances in this paper on the ACE RDC 2004 corpus are evaluated using 5-fold cross validation on the entire corpus .


The training parameters are chosen using cross-validation on the ACE RDC 2003 training data.

λ in our tree kernel is fine-tuned to 0.5.


Concretely, if we find that the current classifier already has acceptably strong confidence on its classification results for all remained unlabeled data, we assume the current training data is sufficient to train the classifier with maximum effectiveness.

if a classifier induced from the current training data has strong classification confidence on an unlabeled example, we could consider it as a redundant example.


To predict when to stop active learning procedure, it is logical to consider the changes of accuracy performance of the classifier as a signal to stop the learning iteration.

during the range predicted by the proposed solution, if the change of accuracy performance of the learner (classifier) is very small, we could assume that the current classifier has reached maximum effectiveness.


Let λ be a parameter vector and f (y s−1 , y s , x) be a (local) feature vector obtained from the corresponding position s given x. CRFs define the conditional probability, p(y|x), as being proportional to a product of potential functions on the cliques.

p(y|x) on a (linear-chain) CRF can be defined as follows: is a normalization factor over all output values, Y, and is also known as the partition function.


Our hybrid model for a structured predictor is designed by the discriminative combination of several joint probability densities of x and y, p(x, y).

the posterior probability of our hybrid model is defined by providing the log-values of p(x, y) as the features of a log-linear model, such that: Here, j=1 represent model parameters of individual models estimated from labeled and unlabeled data, respectively.


The intuitive effect of maximizing Equation (4) is similar to performing 'softclustering'.

unlabeled data is clustered with respect to the R distribution, which also includes information about labeled data, under the constraint of generative model structures.


According to our definition, the Θ and Γ estimations are mutually dependent.

the parameters of the hybrid model, Γ, should be estimated 1.Given training set: 3.Initialize Γ (0) , Θ (0) and t ← 0.


Moreover, the division of the labeled training data and the feature set of the generative models were derived in the same manner as our NER experiments (see Section 4.1).

we divided the labeled training data into 4/5 for estimating Λ and 1/5 for estimating Γ; one feature type shown in    We evaluated the performance in terms of the F β=1 score, which is the evaluation measure used in CoNLL-2000 and, and sentence accuracy, since all the methods in our experiments optimize sequence loss.


In this paper, we focus on model structure.

we are interested in finding a suitable model structure for a given task and training corpus.


We can therefore use simple search methods to look for a suitable model structure for a given task and training corpus.

we have performed several experiments using hill-climbing methods to search for an appropriate model structure for a given task.


This transformational approach has the advantage that we can explore different model structures using off-the-shelf learners, without modifying them.

we can apply the transformation corresponding to a given model structure to the training corpus, and then train the off-the-shelf learner based on that transformed corpus.


As an alternative to requiring that the FST generate every output string, we can define an extended inversion function, that includes the inverted FST, but also generates output values for transformed values that are not generated by the FST.

in cases where the transformed value is not generated by the FST, we can assume that one or more of the transformed tags was chosen incorrectly; and make the minimal set of changes to those tags that results in a string that is generated by the FST.


Before using Equation 1, we must estimate a unigram model from d: that is, an assignment of probabilities Pr(w | d) for all w ∈ V. One way to do this is to use a maximum likelihood estimate (MLE) -an assignment of Pr(w | d) for all w which maximizes the probability of generating d. The MLE is given by the equation is the number of occurrences of w in d, and |d| is the total number of words in d. However, using this formula means we will get a value of zero for Pr(q | d) if even a single query word q i is not found in d. To overcome this problem, we smooth the model by assigning some probability mass to such unseen words.

we adopt a two-stage smoothing method (Zhai and Lafferty, 2004): Here, U denotes a background language model, and µ > 0 and λ ∈ (0, 1) are parameters to the smoothing procedure.


Despite the differences between the tasks, it is worth investigating whether the information that helps disambiguate left and right-bracketings can also be useful for segmentation.

we explored many of the sources of information used by Nakov and Hearst (2005a), as well as several novel features that aid segmentation performance and should also prove useful for NC analysis researchers.


For a decision at position i, we use features from tokens up to three positions to the left and to the right of the decision location.

for a decision between x L0 and x R0 , we extract features from a window of six tokens in the query: ..}.


Of course, a dataset with queries and retrieval scores may serve for more than evaluation; it may provide the examples used by the learning module.

the parameters of the contraction or segmentation scoring function could be discriminatively set to optimize the retrieval of the training set queries.


This is part of an effort to develop a Knowledge Base Management System to benefit neuroscience research.

we are interested in knowledge of various aspects (attributes) of Tract-tracing Experiments (TTE) (data records) in neuroscience.


A careful study of our corpus shows that data records share many words and phrases and are not distinguishable based on word similairties.

different experiments (records) always belong to the same topic and there is no way to segment them using standard topic segmentation techniques (even if one views the problem as a finer-level segmentation than traditional text segmentation).


Rather, they are directional models that model the probability of generating a target sentence given a source sentence.

they only model many-to-one alignments, recovering the many-tomany alignments in a preprocessing step.


Generally, the gap and match factor affect the accuracy of the alignments as expected.

the alignments with the best AMA (0.889) and the best F 1 -measure (0.678) are generated when the gap match factor are set to their natural values (1,1), which theoretically should maximize the expected AMA.


The additional recall is due to the ability of the CRF model to incorporate multiple overlapping features.

the domain-specific features are important for aligning words and phrases that have little or no orthographic similarity.


This paper proposes one possible answer to the first question, explores the second by providing learning curves in the context of a particular statistical machine translation system, and hints that the third may yet be some time in answering.

it proposes a distributed language model training and deployment infrastructure, which allows direct and efficient integration into the hypothesis-search algorithm rather than a follow-on re-scoring phase.


However, we need to be careful about combinatorial explosion of the number of translation options given a sequence of mapping steps.

the expansion may create too many translation options to handle.


Unknown words are a well-known hindrance to natural language applications.

they drastically impact machine translation quality.


This inference procedure shares similarities with the K-nearest-neighbor (k-NN) approach.

since no model of the training material is being learned, the training corpus needs to be stored in order to be queried.


A good reference on the subject is Felsenstein (2003).

yang and Rannala (1997), Mau and Newton (1997) and Li et al.


While quantitative evaluation such as measuring edit distance is helpful for comparing results, it is also illuminating to consider the plausibility of the learned parameters in a historical light, which we do here briefly.

we consider rules on the branch between la and vl, for which we have historical evidence.


we show that our automatic approach outperforms classic systems on the task of phone recognition on the TIMIT data set.

it outperforms standard state-tied triphone models like Young and Woodland (1994), achieving a phone error rate of 26.4% versus 27.7%.


This expression is an approximation because it neglects interactions between instances of the same states at multiple places in the same sequence.

since phones frequently occur with multiple consecutive repetitions, this criterion may vastly overestimate the actual likelihood loss.


We partitioned the TIMIT data into training, development, and (core) test sets according to standard practice (Lee and Hon, 1989;Gunawardana et al., 2005;Sha and Saul, 2006).

we excluded all sa sentences and mapped the 61 phonetic labels in TIMIT down to 48 classes before training our HMMs.


8 ETK was tested using cross validation, so that names in the training set and those in the testing set were disjoint.

all names in the testing set were removed from each collection in the training set.


English For English we used the Wall Street Journal section of the Penn Treebank (Marcus et al., 1993).

we used sections 2-11 for training and a subset of section 23 for testing.


SHIFT: it does not construct dependency, just moves the parsing focus.

the new left focus word is the previous right focus word, whose succeeding sub tree's root is the new right focus word.


The source state of such an edge is determined by the partial parse structure built at the time of the destination state, thereby allowing the conditional dependency edges to be appropriate for the structural nature of the parsing problem.

they allow conditional dependencies to be local in the parse structure, not just local in the history sequence.


The current state is connected to previous states using a set of 7 distinct relationships defined in terms of each state's parser configuration, which includes of a stack and a queue.

the current state is related to the last previous state whose parser configuration has: the same queue, the same stack, a stack top which is the rightmost right child of the current stack top, a stack top which is the leftmost left child of the current stack top, a front of the queue which is the leftmost child of the front of the current queue, a stack top which is the head word of the current stack top, a front of the queue which is the current stack top.


dependencies that are adjacent to the main dependency of the factor.

these approaches considered sibling relations of the modifier token (Eisner, 1996;McDonald and Pereira, 2006).


In this paper we extend the parsing model with other types of second-order relations.

we incorporate relations between the head and modifier tokens and the children of the modifier.


The higher-order model defined in this paper decomposes a dependency structure into factors that include children of the head and the modifier.

a factor in our model is represented by the signature f = h, m, l, c h , c mi , c mo where, as in the first-order model, h, m and l are respectively the head, modifier and label of the main dependency of the factor; c h is the child of that is furthest from m; c mo is the child of m outside [h .


In the graph-normalized model, we assume that the conditional distributions are independent of one another.

we find the model parameters that maximize the likelihood of p(g * |d), where g * is the correct parent in the training data.


We per-form the optimization over the entire training set, tying the feature parameters.

we perform maximum entropy (MaxEnt) estimation over the conditional distribution using second-order gradient descent optimization techniques.


There are other tests that more accurately apply to our small tables with only two rows and columns.

fisher's exact test calculates probability of the observed table using the hypergeometric distibution.


Since we are using thresholds of the negative logarithm of the pvalue, the value α = log(N ) is a useful threshold to consider.

α + (where is an appropriately small positive number) is the smallest threshold that results in none of the 1-1-1 phrase pairs being included.


Another question that came up is the role of phrase pairs that occur only once: C(s,t) = 1.

as discussed above, the most significant of these are the 1-1-1 phrase pairs whose components also only occur once: C(s) = 1, and C(t) = 1.


This representation enables fast lookup of any contiguous substring using binary search.

all occurrences of a length-m substring can be found in O(m + log |T |) time (Manber and Myers, 1993).


In this paper we present an empirical study on how different ensembles of translation outputs affect performance in system combination.

we will address the following questions: • To what extent can translation quality benefit from combining systems developed by multiple research labs?


), with the senses of want and need in the fourth sense.

these two senses correspond to WordNet 2.1 senses need#v#1 and need#v#2, respectively, which are not clustered according to the SENSEVAL-2 word-specific labeling for needneed#v#1 is listed as a singleton (i.e., unclustered) sense, though need#v#2 is clustered with need#v#3, "have or feel a need for."


In a sense, posterior inference reverses the process described above.

given a document collection w 1:D , the full posterior is where the constant of proportionality is the marginal likelihood of the observed data.


LDAWN is a Bayesian framework, as each variable has a prior distribution.

the Dirichlet prior for β s , specified by a scaling factor S and a normalized vector α s fulfills two functions.


They have found that the large majority of failures is caused by missing lexical entries, with 40% of the cases, and missing constructions, with 39%, where missing MWEs accounted for 8% of total errors.

even by a margin, the lexical coverage is lower than the grammar construction coverage.


The other two distributions show a visible effect in the form of a slight displacement of the distributions to the left for MWEs.

for the distribution of PE, the large peak on the right, representing the n-grams whose word order is irrelevant with respect to its occurrence, has an important reduction for MWEs.


In this paper we looked at some of the issues involved in the evaluation of the identification of MWEs.

we evaluated the use of three statistical measures for automatically identifying MWEs.


This means that the backward model builds much more partial structure in the stack as it consumes input tokens, while the forward model must consume most tokens before it starts making attachments.

context in general in the backward model has more structure, and attachments are made while there are still look-ahead tokens, while the opposite is generally true in the forward model.


The number of learning iterations over the training data, which is the only adjustable parameter of the algorithm, was determined by cross-validation.

in order to overcome the limitations of a linear perceptron, we introduce a feature map Φ: that maps a feature vector x into a higher dimensional feature space consisting of all unordered feature pairs: we expand the original representation in the input space with a feature map that generates all second-order feature combinations from each observation.


Note that the edge is coming from the parent to the child.

given a child node word j , we are finding the parent, or the head word i such that the edge (i, j) has the highest weight among all i, i = j.


The main reason is that the step-back might trace back to previous words, which can be viewed as pop the top words on the stack back to the unparsed strings, while the Nivre's method does not trace-back any two words in the stack.

if a word is pushed into the stack, it will no longer be compared with the other deeper words inside the stack.


The figure represents a small portion of the observed sequence of decisions that constitute the recovery of a parse tree, indicated by the observed states D i .

it illustrates the pattern of connectivity for decision d t k .


A VP group is a stack of VPs covering the same compound verb group, such as Formal definitions are given below: true if VP ∈ path(v, srn) and VP ∈ VP-group directly dominating w f alse otherwise In addition to the minimality conditions, which resolve ambiguity when two predicates compete to govern an argument, we use locality constraints to capture distinct local relationships between a verb and the syntactic position occupied by a candidate argument.

we distinguish between internal arguments occupying a position dominated by a VP node, external arguments occupying a position dominated by an S node, and extracted arguments occupying a position dominated by an SBAR node.


It would be sufficient to define l to be the pair POS, A -the inclusion of η h , η m and L adds redundant information that can be recovered from the set E, and other dependencies in D-but it will be convenient to include this information in the label.

it is important that given this definition of l, it is possible to define a function GRM(l) that maps a label l to a triple of nonterminals that represents the grammatical relation between m and h in the dependency structure.


A set of corresponding experiments with automatic features gave significant improvement from the addition of individual features and a cumulative effect of the same features in combination.

we show that the very same tendencies in improvement for specific argument relations such as subjects, objects and predicatives may be obtained using automatically acquired features.


VPCs are combinations of verbs and particles, such as take off in Our plane took off late, that due to their complex characteristics and flexible nature, provide a real challenge for NLP.

there is a lack of adequate resources to identify and treat them, and those that are available provide only limited coverage, in face of the huge number of combinations in use.


(2003), Bannard (2005) and Fazly and Stevenson (2006)).

fazly and Stevenson (2006) look at the correlation between syntactic fixedness (in terms of e.g.


Here, we use this computational model to investigate fast mapping and its relation to word learning.

we take a close look at the onset of fast mapping in our model by simulating some of the psychological experiments mentioned above.


1 For a symbol m ∈ S (t) and a word w ∈ U (t) , the higher the probability of m being the meaning of w (according to p(m|w)), the more likely it is that m is aligned with w in the current input.

a(w |m, U (t) , S (t) ) is proportional to p (t−1) (m|w ).


We assume that the child knows the meaning of verbs and determiners such as get and the, therefore we simplify the familiar target condition in the form of the following input item: A familiar word such as ball has a meaning probability highly skewed towards its correct meaning.

upon hearing ball, the model can confidently retrieve its meaning ball, which is the one with the highest probability p(m|ball ) among all possible meanings m. In such a case, if ball is present in the scene, the model can easily pick it as the referent of the familiar target name, without processing the other objects in the scene.


for a target word, as well as the referent probability of a target word for its correct meaning, increase as a result of more training on the context.

a more familiar context helps the model perform better in a fast mapping task.


We examine the transcription system Brent (1999) uses and conclude some unorthodox choices were made when transcribing the corpus.

some phonemes that are normally considered distinct are combined into one symbol, which we call a bi-phone symbol.


We constructed a learning corpus by randomly selecting in this database 200,000 words and their gender such that their distribution is akin to their frequency distribution in Manulex.

the probability of picking a given word in the corpus is just its frequency.


Although verb position is ultimately a more powerful representation of word order for English sentences, it does not accurately reproduce a 21-month-old's performance on all aspects of this task.

the VPosition feature does not support the overgeneralization of the A0A1 pattern to the 'A and B gorp' test sentences.


The first is a simple maximum likelihood estimate, which enforces a preference for creating clusters of frames with the same context words.

head words in the same cluster will tend to share the same adjacent words.


For example, if w i can be used as a noun or a verb, then we want the likelihood of seeing other nouns or verbs in position i of frames in cluster k. Here, we use the partial knowledge of the learned clusters.

we look over all existing clusters k , estimate the probability that w i is the head word of frames in k , then estimate the probability of using the head words from those other clusters in position i in cluster k. We refer to this category-based estimate as P cat : where P i (k |k) is the probability of finding usages from cluster k in position i given cluster k. To support this we record the categorization decisions the model has made.


For the six sets of test words, we measure the effect of placing each of these words in both noun and verb contexts.

each word in each condition was used as the head word in each of the 500 noun and 500 verb disambiguating frames.


In (Michelbacher et al., 2007), the authors clearly point at the importance of asymmetry in Natural Language Processing.

we deeply believe that asymmetry is a key factor for discovering the degree of generality of terms.


For the first task, we use the implementation of the k-means algorithm of the NLTK toolkit 5 .

we bootstrap the k-means by choosing the initial means as follows.


It is defined in Equation 14where d is the distance between every pair of words in the list ordered with TextRank and the reference list which is ordered according to WordNet or the Web and n is the number of pairs of ranked words.

the Spearman's rank correlation coefficient is a number between -1 (no correlation at all) and 1 (very strong correlation).


However, they cannot be discarded so easily.

we aim at looking at web counts in web directories that would act as specific domains and would reduce the space for ambiguity.


We approach the problem of word sense disambiguation through a relatively straightforward method that incorporates ideas from the token, type, and selector approaches.

we expand the use of selectors in several ways.


For our purposes, a similarity measure is used for nouns which may take the place of a target word within its local context, while words which commonly appear in other parts of the local context are measured by relatedness.

the similarity measure places emphasis strictly on the is-a relationship.


The ABC positioning of icons allows users to infer the semantic role of the corresponding concepts.

we found that verbs can be difficult to depict and understand without such hints.


Such a layout is particularly amenable to machine learning.

we can turn the problem of finding the optimal layout for an input sentence into a sequence tagging problem, which is well-studied in NLP.


[Subjects]: To partially overcome the first challenge, we recruited two groups of subjects with medium and high literacy respectively, in hopes of extrapolating our findings towards the low literacy group.

the medium group consisted of seven non-native English speakers who speak some degree of English-"medium literacy" refers to their English fluency; twelve native English speakers comprised the high literacy group.


It can be further refined, however, by considering the prevalence of the inflected forms.

a form generated with more common affix transformations should be given greater weight than one generated with less common ones.


What all the above methodologies do not provide is a means to disambiguate morphological analysis for machine translation based on the words' contexts.

for an Arabic word analysis of the form prefix*-stem-suffix* a morpheme only is either always retained, always dropped off or always merged to the stem regardless of its surrounding text.


We need an evaluation method to prevent our program from generating bad phrase trees.

good phrase trees should be given a higher priority.


Each translation hypothesis is associated with a phrase-tree node.

a phrase-tree node has a collection of translation hypotheses.


At generation time, we find all possible realizations of each attribute value in the input attribute set, and for each combination of attribute realizations, we find the most likely set of dependencies and precedences given the training data.

we bin the selected attribute realizations according to whether they are most likely to precede, depend on and precede, depend on and follow, or follow, the head word they are closest to.


Lastly, we analyze systems based on the ratio between labeled F 1 score for semantic dependencies and the LAS for syntactic dependencies.

this measure normalizes the semantic scores relative to the performance of the parsing component.


To choose previous relevant decisions, we make use of the partial syntactic and semantic dependency structures which have been decided so far in the parse.

the current latent state vector is connected to the most recent previous latent state vectors (if they exist) whose configuration shares a node with the current configuration, as specified in Table 1.


Also related to efficiency, we apply syntactic and semantic filters in order to reduce the number of score evaluations.

the set of assignable labels is filtered by the POS of the head and modifier (discarding all labels not previously seen in the training corpus between words with the same POS).


For  Figure 1: System Architecture the semantic roles, we extracted features from the previous stage, combined with deep parsing results (in MRS), and use statistical classification models to make predictions.

the second part can be further divided into four stages: predicate identification (PI), argument identification (AI), argument classification (AC), and predicate classification (PC).


Assuming no predicates overtly known, we keep using a word-pair classifier to perform semantic parsing through a single-stage processing.

we specify the first word in a word pair as a predicate candidate (i.e., a semantic head, and noted as p in our feature representation) and the next as an argument candidate (i.e., a semantic de- c preddir: the direction to the current predicate candidate.


For predicate, only nouns and verbs are considered possible candidates.

all words without a split PoS in these two categories are filtered out.


Firstly, the current processed semantic role labels and argument candidate direction are checked.

they are the arguments A0 and A1 that have been marked before the predicate candidate p and the current argument identification direction after p. Secondly,   each example 2 of p in NomBank or PropBank that depends on the split PoS tag of p is checked if it partially matches the current processed role labels.


We are using a subset of feature set described in Nivre (2007).

3 we had to discard all features from Nivre's set that refer to a word right to the current word in order to retain our strict incremental behavior.


Note that for this experiment and all others reported below, we used the default settings of the opennlp MEM trainer.

this means that 100 iterations were used in all training runs and that for all experiments no tuning of parameters and smoothing was done, basically because we had no time left to exploit it in a sensible way.


This raises the question of finding more effective smoothing techniques.

the jump from specific frames to coarse parts of speech is probably not optimal.


Each module is developed with maximum entropy classifiers based on different predicate types.

dependency relation compression method and extracted path patterns are used to improve the performance in the argu-  Table 3: Semantic scores of global sequence selection in WSJ test set.


Due to time limitation, a simple algorithm is developed to identify noun and verb predicates: 1) For the WSJ corpus, we simply adopt the annotations provided by PropBank and NomBank.

we only consider the verb and noun predicates annotated in PropBank and NomBank respectively.


Experiments on the CoNLL 2007 corpora (Dell'Orletta, 2008) have shown that this indeed occurs, especially for distances in the range from 6 to 23.

the most significant improvements are for dependencies with label COORD (+ 6%) and P (+ 8%).


We now define a log-linear model which assigns a probability to each candidate simple labeling based on its score.

the probability of a simple labeling c sv k with respect to a weight vector w is P (c sv k ) = Unfortunately, we do not have labeled examples of correct simplifications.


The second is incorporating uncertainty from the parser into our model.

we would like to extract a complete parse forest from the Charniak parser and use it as input to our model.


As an instance of a more relaxed projection of complete structures, we also implemented a fallback to unidirectional links which projects further dependencies after a partial structure has been built based on the more reliable bidirectional links.

the dependencies established via unidirectional alignments are constrained by the existing subtrees, and are subject to the wellformedness conditions for dependency trees.


We use the CoNLL-X data format for dependency trees (Buchholz and Marsi, 2006) to encode partial structures.

every fragment root specifies as its head an artificial root token w 0 (distinguished from a true root dependency by a special relation FRAG).


Since MaltParser scores local decisions, this simply amounts to suppressing the creation of SVM training instances for such nodes (U and gelijk in (1)).

where the feature model refers to context information, unattached words provide this information (e.g., the feature vector for volkomen in (1) contains the form and POS of gelijk), but there are no instances indicating how they should be attached themselves.


The most innovative aspect of our work is the attempt to model and implement a solution in the context of structural kernels.

we focus on Tree Kernel (TK) functions, which are especially interesting for the Computational Linguistics community as they can effectively encode rich syntactic data into a kernelbased learning algorithm.


With the recent development of fast fully unsupervised parsers, it is now possible to add parserbased information to lexical patterns while keeping the language-independence of the whole framework and still avoiding heavy computational costs.

we incorporate the bracketings induced by the parser into the meta-patterns used by a symmetric patterns and graph-based unsupervised concept discovery algorithm.


Our method utilizes the information induced by unsupervised parsers.

we make use of the bracketings induced by Seginer's parser 1 (Seginer, 2007).


However, the fact that it does not consider any syntactic information causes problems.

it does not consider the constituent structure of the sentence.


We utilize bracketing boundaries in our meta-patterns in addition to HFW and C slots.

their original meta-patterns are totally lexical, while ours are lexico-syntactic meta-patterns.


This paper describes the particular instance of the IFSC protocol where semantic information is introduced through abstraction of lexical terms in the feature space with their SRWL labels.

there are two notable contributions of this work: (1) an interactive method for the expert to directly encode semantic knowledge into the feature space with minimal effort and (2) a querying function which uses both the current state of the learner and properties of the available SRWLs to select informative instances for presentation to the expert.


In this paper we address the problem of identifying reciprocal relationships in English.

we introduce an algorithm that semi-automatically discovers patterns encoding reciprocity based on a set of simple but effective pronoun templates.


In this paper we present an analysis of the concept of reciprocity as expressed in English and present a way to model it.


To the best of our knowledge, the rest of our reciprocity model is novel.

we use a novel procedure which extracts pairs of reciprocal instances and present two novel unsupervised clustering methods which group the instance pairs in meaningful ways.


Taking into account the frequencies of the subsequences occurring within instances of each partition, we use the following recursive scoring function (n is the length of each subsequence of size n): In addition, in order to ensure a valid ranking over the extracted templates with different lengths for each partition, we need to normalize the scores obtained for PART1, PART3, and PART5.

we need to scale the scores obtained for each partition to discount the scores of longer partitions, so that the maximum possible score would remain the same regardless of how long the partition is.


While C is not terminal, apply f (C) to C. It is worth noting that search will always terminate once a terminal configuration has been reached, even though there is nothing in the transition system that forbids transitions out of terminal configurations.

while the model itself allows regressions and refixations after the last word of the text has been fixated, the search algorithm does not.


First, Barzilay and Elhadad (2003) and Nelken and Shieber (2008) align like-chunks between the two documents.

they are either aligning sentences to sentences or paragraphs to paragraphs.


If the addition of query expansion enables an aligner to correctly align slide regions which its query expansionless counterpart could not, then we should see large percentages of errors being corrected when comparing aligner A to B and C to D. By comparing aligner A to C and B to D, we have a measure of the error reduction achieved by implementing scoring method 2 instead of method 1.

tables 8 and 9 show that aligner D significantly reduced aligner B's errors and aligner C significantly reduced aligner A's errors, but aligner B did not improve much on A, nor did D on C. adding query expansion did not significantly reduce errors, but using scoring method 2 instead of 1 did.


We analyze some of the fundamental design challenges and misconceptions that underlie the development of an efficient and robust NER system.

we address issues such as the representation of text chunks, the inference approach needed to combine local NER decisions, the sources of prior knowledge and how to use them within an NER system.


These issues led us to report token-level entity-identification F 1 score for this dataset.

if a named entity token was identified as such, we counted it as a correct prediction ignoring the named entity type.


When making the prediction for token instance x i , we record the label assignment distribution for all token instances for the same token type in the previous 1000 words.

if the token instance is "Australia", and in the previous 1000 tokens, the token type "Australia" was twice assigned the label L-ORG and three times the label U-LOC, then the prediction history feature will be: (L − ORG : 2 5 ; U − LOC : 3 5 ).


We observed very similar patterns in all other sentence lengths in WSJ20 and NEGRA20 for both Average F-score and Constituent F-score.

for every sentence length in both corpora, PUPA outperforms SEPA and MC in terms of both measures.


Second, we compare the behavior of V, VI and NVI in various situations to the desired behavior and to each other.

we show that V gives high scores to clusterings with a large number of clusters even when they are of low quality.


When the number of classes increases, the numerator decreases and the denominator increases, and hence the score decreases.

as the real solution gets closer to the singletons solution, the score decreases, which is the correct behavior.


In the class splitting scenario (see convex additivity definition in Section 3) it holds that N V I(C, K) = .

like for VI, the impact of splitting or merging a cluster on NVI is limited only to those clusters involved, and its size is relative to the size of these clusters.


In this section we provide a synthetic example that demonstrates an undesireable behavior of V (and NVIK) not manifested by VI and NVI.

v favors solutions with a large number of clusters, giving them higher scores than to solutions that are evidently superior.


These 2774 labels annotate 14.4% of compared constituents, and the 2864 labels that annotate less than 100 constituents each, cover 30.7% of the compared constituents (these percentages are not shown in the table).

mDL is not a solution in which almost all of the mass is concentrated in the few leading clusters; its tail occupies a large percentage of its mass.


Second, if two words appear in the lexical resource but not in the hypernym relation of that same resource then we will assume that they are unrelated.

we assume the hypernymy relation specified in the lexical resource as complete (like in the work of Snow et al.


The underlying assumption is that term correlations are captured by the co-occurrence information.

two terms are semantically related if they cooccur often in the same documents.


In point of fact, Krishnan and Manning (2006) present an alternative to these graph-based methods.

they break the explicit links that mutually condition non-adjacent lexemes, and instead rely on separate passes in a way that is reminiscent of earlier methods.


In contrast to previous approaches, what is needed is not so much a way of coordinating non-local decisions about an entity's label, as a way of coordinating non-local evidence pertinent to the labeling decision.

instead of conditioning the labeling decision of a lexeme on the labeling decisions for that lexeme elsewhere in the corpus, we ought to condition the decision on the key evidence supporting those decisions.


Each of those features is then duplicated: for a given token α, one version of the feature applies directly to α, while the other version applies to all other instances where α's word form appears in the current document.

what we duplicate is the indicator function for a feature.


To select predictive features to displace, we've had most success with a method based on information gain.

we use a one-time preprocess that measures feature gain relative to a corpus.


Note finally that the feature schemas we consider for displacement only encode local evidence (see Table 2 below).

they do not encode the assigned label of a word form, as this would effectively introduce the kind of graphical conditional dependencies that lie outside the scope of linear-chain CRF methods.


What is apparently happening is that these three data sets are well matched to a group of linguistically inspired lexical features with which we trained our baseline CRF.

our baseline features include gazetteers and word lists hand-selected for identifying entities based on local context: first names, agentive verbs, date atoms, etc.


As noted earlier the CoNLL standard diverges in several ways from MUC-6.

coNLL calls for a MISc entity that covers a range of name-like entities, e.g., events.


We represent the entities using features extracted from the textual contexts in which they occur.

we use a search engine to collect such contexts from the Web.


As a result, we obtain a generalized similarity function between multi-contexts that incorporates semantic relations between terms, automatically learned from unlabeled data.

we use Wikipedia to build the latent semantic space.


Formally, the kernel is a function k : X × X → R that takes as input two data objects (e.g., vectors, texts, parse trees) and outputs a real number characterizing their similarity, with the property that the function is symmetric and positive semi-definite.

for all where φ is an explicit mapping from X to an (inner product) feature space F. In the next sections, we define and combine different kernel functions that calculate the pairwise similarly between multi-contexts.


Experiments showed that the integration of encyclopedic knowledge consistently adds useful information when compared to baselines that rely exclusively on the text at hand.

when tested on a data set consisting of documents manually annotated with categories by Wikipedia users, the topics identified by our system were found useful as compared to the manual annotations.


In recent years, the integration of manually-built lexical resources into NLP systems has received growing interest.

resources annotated with the surface realization of semantic roles, like FrameNet (Baker et al., 1998) or PropBank (Palmer et al., 2005) have shown to convey an improvement in several NLP tasks, from question answering (Shen and Lapata, 2007) to textual entailment (Burchardt et al., 2007) and shallow semantic parsing (Giuglea and Moschitti, 2006).


In contrast to the previous year, the evaluation data indicated which words were to be dealt with (for the SRL task).

(predicate) disambiguation was still part of the task, whereas the identification of argument-bearing words was not.


The conversion procedure identifies the head by first exploiting the structural information in the syntactic parse and detecting six broad categories of syntactic relations that hold between the head and its dependents (predication, modification, complementation, coordination, auxiliary, and flat) and then designating the head based on these relations.

the first conjunct of a coordina-tion structure is designated as the head and the heads of the other conjuncts are the conjunctions preceding them.


We considerably simplified the annotations provided by SALSA, which use a rather complex annotation scheme.

we removed annotation for multi-word expressions (which may be non-contiguous), annotations involving multiple frames for the same predicate (metaphors, underspecification), and inter-sentence roles.


Due to the differences between the seven languages, we use different parameters to train a parsing model.

as Czech and German languages are none-projective and the others are projective, we train Czech and German languages with parameter "none-projective" and the others with "projective".


To train CoNLL-specific parse selection models, we parsed the development sections in 500-best mode (using the existing models) and then mechanically 'annotated' the HPSG analyses with maximum DA as preferred, all others as dis-preferred.

this procedure constructs a 'binarized' empirical distribution where estimation of log-linear model parameters amounts to adjusting conditional probabilities towards higher DA values.


Due to variations both in language characteristics and annotation decisions across corpora, these differences between syntax and semantics vary across the seven languages, but they are consistent enough to motivate the development of new techniques specifically for handling semantic dependency structures.

we use a different method for parsing crossing arcs.


We keep using a word-pair classification procedure to formulate semantic dependency parsing.

we specify the first word in a word pair as a predicate candidate (i.e., a semantic head, and noted as p in our feature representation) and the next as an argument candidate (i.e., a semantic dependent, and noted as a).


We had to use a special strategy for the Czech data: we selectively chose wordpredicate pairs for generating the training dataset.


The step of predicting senses can use the arguments that have been predicted in the previous step.

the list of all arguments that have been found is added and may be used as a test in any feature.


Motivated by this observation we define our meaning derivation process over the rules of the MR language and use syntactic information as a way to bias the MR construction process.

our inference process considers the entire space of meaning representations irrespective of the patterns observed in the training data.


By using a dynamic programming table that stores the values of φ(u) for all u ∈ Sub(w) we can compute this in time O(|K| 2 |F ||w| 3 ); this is just an elementary variant of the CKY algorithm.

we define the language defined by the DLG G to be to say, a string is in the language if we predict that a string has the context (λ, λ).


The results presented here lack a couple of technical details to be completely convincing.

we would like to show that given a representation of size n, we can learn once we have seen a set of examples that is polynomially bounded by n. This will be challenging, as the size of the K we need to converge can be exponentially large in F .


This algorithm can be implemented directly and functions as expected on synthetic examples, but would need modification to run efficiently on natural languages.

rather than considering whole contexts of the form (l, r) it would be natural to restrict them just to a narrow window of one or two words or tags on each side.


As they do, we have used the Penn treebank (Marcus et al., 1994) for our experiments, employing the syntactic annotations that it provides for evaluation purposes only.

we have used WSJ10, composed of 6842 sentences, which is the subset of the Wall Street Journal section of the Penn Treebank, containing only those sentences of 10 words or less after removing punctuation and null elements, such as $, ", etc.


First, at initialization, words are randomly assigned to clusters in a way that cluster sizes are distributed according to the Zipfian distribution (with a parameter s).

we randomly select words to be assigned to the first cluster until the fraction of word types in the cluster equals to the prediction given by Zipf's law.


Second, it may be valuable to decouple cor-pus statistics from the induced clustering when the latter is to be used for annotating corpora that exhibit different statistics.

if we evaluate an algorithm that will be invoked on a diverse set of corpora having different token statistics, a type level evaluation might provide a better picture (or at least a complementary one) on the quality of the clustering algorithm.


However, the score should surely depend on r (the size of each word's gold entry).

when r = |C| we get that the induced and gold clusterings are identical.


To be able to use this technique, edge weights must not depend upon h. In 1-1 mapping, |h(B i )| = |B i |, and therefore F (h) = F and w i (h) = w i .

both quantities are independent of h 3 .


It turns out that, depending on the cost function, some variables may a priori be excluded from the optimization problem without losing global optimality.

they can be excluded even before the first LP-relaxation is solved.


In our first set of experiments, we compute Viterbi alignments merely to evaluate the quality of the standard training process.

the model parameters are updated based on the alignments found by hillclimbing.


This usually involves some form of prioritized data collection.

one would like to construct the smallest possible parallel training corpus that achieves a desired level of performance on unseen test data.


Thirdly, because the original terminate criterion requires an error estimation process in each iteration, we adapt the much simpler terminate criterion of standard Co-training into our algorithm, which iterates until all the unlabeled data are finally labeled or the candidate sets do not change since the last iteration.

our algorithm inherits both the benefits of using three classifiers and the simplicity of using Co-training style termination criterion.


were selected to carry out this process.

the Annie English Tokeniser, Hash Gazetter, RegEx Sentence Splitter and the Stanford Parser modules in Gate are used to analyze the input.


On the other hand, the quantifiers are words considered in sentiment analysis as amplifiers or downtoners (Quirk et al., 1985).

to say, the word very in the sentence "a very good idea" amplifies the intensity of the emotional meaning and the positivity of the sentence, while the word less in the sentence "It is less handsome than I was expecting" dismisses its intensity and polarity.


Labeling 975 new synsets significantly improves the performance of our system in both datasets and for all ML techniques.

the best improvement is achieved by the Logistic classifier: from 52.7% to 72.4% of accuracy in the news dataset, and from 50.5% to 61.5% of accuracy in the movies dataset.


The influence of the different coreference algorithms comes from the entity types that are used to determine coreference chains, and that also correspond to WordNet candidate synsets.

although the final coreference chain may be predicted by two different models, the synsets they use to do so may differ, affecting the synset and hypernym features used for ontological prediction.


We have a set Eachx is an N -dimensional attribute vector, and y ∈ {1, ..., K} are classes.

a classifier, H, maps an attribute vector,x, to a class, y. H is parameterized by a K-by-N matrix of weights, W: whereW r is the rth row of W. the predicted label is the index of the row of W that has the highest inner-product with the attributes,x.


Since the unsupervised system in (5) has zero weight variance, the SVM learned in (6) should do as least as well as (5) as we tune the C-parameter on development data.

as C approaches zero, variance minimization becomes the sole objective of (6), and uniform weights are produced.


We use each of the category sets introduced in Section 3.2 to label a word usage in the test set, without using the word form itself as a feature.

we assume that the target word is unknown, and find the best category for it based only on its surrounding context.


One facet of our proposed strategy for enhancing information access to troubleshooting-oriented web user forum data is to preprocess threads to uncover the "content structure" of the thread, in the form of its post-to-post discourse structure.

we identify which earlier post(s) a given post responds to (linking) and in what manner (tagging), in an amalgam of dialogue act tagging (Stolcke et al., 2000) and coherence-based discourse analysis (Carlson et al., 2001;Wolf and Gibson, 2005).


Our first experiment is based on post-level dialogue act (DA) classification, ignoring link structure in the first instance.

we predict the labels on edges emanating from each post in the DAG representation of the post structure, without specifying the edge destination.


The algorithm sketched above is general enough to be suitable for any distributed system, but when using a system with shared memory (e.g., a single multiprocessor machine) a more efficient implementation is possible.

we can avoid the master/slave architecture and simply start p threads that each compute and execute updates independently, with a synchronization lock on θ.


A solution to both problems is the application of feature selection techniques.

the problem of feature selection in Tree Kernel (TK) spaces has already been addressed by previous work in NLP, e.g.


In this paper, we present and empirically validate a theory which aims at filling the abovementioned gaps.

we provide: (i) a proof of the equation for the exact computation of feature weights induced by TK functions (Collins and Duffy, 2002); (ii) a theoretical characterization of feature selection based on w .


| w • x| ≥ γ, ∀ x ∈ X (training set), the error of c is bounded by b/ + k R 2 γ 2 log 2 + log 1 δ with a probability 1 − δ, where = |X | and b is the number of examples with margin less than γ.

if X is separated with a margin γ by a linear classifier, then the error has a bound depending on γ.


On the other hand, when marking the scopes of speculative keywords, the scope was extended to the largest syntactic unit possible.

all constituents that fell within the uncertain interpretation were included in the scope.


The aim of Task1 was to develop automatic procedures for identifying sentences in texts which contain unreliable or uncertain information.

this task is a binary classification problem, i.e.


A scope-level F β=1 measure was used as the chief evaluation metric where true positives were scopes which exactly matched the gold standard cue phrases and gold standard scope boundaries assigned to the cue word.

correct scope boundaries with incorrect cue annotation and correct cue words with bad scope boundaries were both treated as errors.


In related work, Hyland (1996) associates the use of speculative language in scholarly publications with the purpose for which they are employed by the authors.

he distinguishes content-oriented hedges from readeroriented ones.


This illustrates that ambiguous hedge cues are frequently used in "certain" sentences.

the baseline algorithm has less true positives than false positives, i.e.


This provides motivation to consider other refinements of our system.

we believe that it might be possible to improve the recall of our system by enriching the list of input features using a lexical ontology in order to extract synonyms for verbs, adjectives and adverbs occurring in the current hedge cue list.


Therefore, using a similar amount of training data for BioScope abstracts as used for BioScope full articles, we still have a difference of 8% between the F-score values obtained.

our system is more efficient on abstracts than on full articles.


This provides motivation to consider other refinements of our system, by incorporating syntactic or semantic information.

we believe that the recall score of our system can be improved by identifying a list of new potential hedge cues using a lexical ontology.


3 Hedge detection with average perceptron The first subtask is to identify sentences containing uncertainty information.

this subtask is a binary classification problem at sentence-level.


Third, encoding the information of the dependency tree in features for the learner is not a straightforward process.

some errors in resolving the scope are caused by keeping subordinate clauses within the scope, as in sentence (2), where, apart from not identifying speculated as a cue, the system wrongly includes resulting in fewer high-confidence sequence assignments within the scope of may.


However, it appears that a nuanced approach based on cue categories could enhance the results further.

the current mechanism does not contribute much to resolving scopes of adverbial cues.


The structured margin is defined in terms of a score function S : X × Y → , in our case the linear score function S(x, y) = w T Φ(x, y), where w ∈ m is a vector of parameters and Φ : X ×Y → m is a joint feature function.

the learning problem then amounts to finding parameters w such that S(x, y x ) ≥ S(x, y) + ∆(y x , y) for all y ∈ Y x \ {y x } over the training data D. we want the score of the correct label to be higher than the score plus the loss, of all other labels, for each instance.


For the first task, hedge detection, we formulate it as a hedge labeling problem, while for the second task, we use a two-step labeling strategy, one for hedge cue labeling and the other for scope finding.

various kinds of syntactic features are systemically exploited and effectively integrated using a large-scale normalized feature selection method.


The "Indicator"s tagging from "suggesting" to "mitogenesis" in Table  4 mean that no other than the situations of a) "Begin" is after or at "suggesting" and "End" is before or at "mitogenesis" b) Both "Begin" and "End" are before "suggesting" c) Both next "Begin" and next "End" are after "mitogenesis" can be accepted.

new labeling should keep the projections of scopes in the result.


We attempt only the Task 1 of the CoNLL shared task which was to identify sentences in texts which contain unreliable or uncertain information.

the task is a binary classification problem, i.e.


We have participated in the in-domain and cross-domain challenges of Task 1.

the aim of Task 1 is to identify sentences in texts that contain unreliable or uncertain information, and it is formulated as a binary classification problem.


The number of such errors is much greater than the number of errors of predicting certain ones as uncertain.

fN is greater than fP in our experiments and the recall ratio is very low, especially for the Wikipedia data set.


In this paper, we demonstrate that morphological analysis can utilize this connection without assuming access to full-fledged syntactic information.

we focus on two aspects of the morphosyntactic connection: • Morphological consistency within POS categories.


After that, we generate each stem σ 0 , prefix σ − , and suffix σ + independently: Token-POS Model This model captures the dependencies between the syntactic categories of adjacent tokens with a first-order HMM.

conditioned on the type-level assignments, we generate (unsegmented) tokens w and their POS tags t: where the parameters of the multinomial distributions are generated by Dirichlet priors: Here, W t refers to the set of word types that are generated by tag t. conditioned on tag t, we can only generate word w from the set of word types in W t which is generated earlier (Lee et al., 2010).


In our approximation scheme, for each possible segmentation and tag hy- , we only consider one possible value for L, which we denote the minimal lexicons.

hence, the total number of hypothesis that we have to consider is only as large as the number of possibilities for ( we recover the minimal lexicons as follows: for each segmentation and tag hypothesis, we determine the set of distinct affix and stem types in the whole corpus, including the morphemes introduced by segmentation hypothesis under consideration.


Relative to a hypothesis that introduces none, this one incurs an additional cost of (1 − γ 0 ) and γ l (1 − γ l ) |σ 0 | .

the hypothesis is penalized for increasing the stem lexicon size and generating a new morpheme of length |σ 0 |.


Note that all five constraints are nested.

this means that it does not make sense to combine them, for a given annotation [x, y], since the result would just match the strictest one.


The human ability to segment continuous sequences extends to non-linguistic domains as well, which further strengthens the general chunking account, as these chunking algorithms have been successfully applied to a diverse array of non-linguistic sequences.

bVE combines the power of the information-theoretic chunk signature with a bootstrapping capability to achieve high levels of performance in both the continuous and incremental paradigms.


The features used to predict whether a triggerargument pair should be classified as a Theme are extracted from the syntactic dependency path and the textual string between them.

we extract the shortest unlexicalized dependency path connecting each trigger-argument pair, allowing the paths to follow either dependency direction.


A direct comparison between learning frameworks is difficult due to the differences in task decomposition and feature extraction.

event extraction results depend substantially on the quality of the syntactic parsing.


A set S, of 49 base streaming models predicted the has-location, L ∃ , feature and categories, c 0,...,47 .

unlike the main model, R, which predicts the 'Actionable'/'Not Actionable' division, each model S predicts either the existence of a location within the text or binary per-category membership.


Analysis of the messages showed that many non-actionable messages were not related to emergencies at all (general questions, requests for work, etc), as were many messages without identifiable locations.

people who tended to not send actionable information also tended to not include locations.


Applying ROC analysis, the methods here could speed up the prioritization of actionable messages by a factor of 10 to 1 based on content alone.

on average an actionable message falls within the 90th percentile for probability of being actionable.


Per-gender analysis in Table 1 reveals interesting insights into different approaches.

we find that Gender Genie and Gender Guesser are biased toward male authors, attributing the majority authors as male.


as expected, the performance of all models drop significantly in this scenario.


However, even manually developed subjectivity lexicons have significant degrees of subjectivity sense ambiguity (Su and Markert, 2008;Gyamfi et al., 2009).

many clues in these lexicons have both subjective and objective senses.


uses a similar rule to make the contextual polarity classifier sense-aware.

the rule is applied to the output of the first step (N/P classifier).


We can think of this third classifier as the learning counterpart of the manual rules from Section 3.2, since it actually learns when to flip the contextual classifier's output considering SWSD evidence.

this merger classifier relies on four machine learning features (1), (2), (3), (4) from above (the ex-   Table 5: N/P classifier with learned SWSD integration act same information used in rule-based integration).


To be more concrete, rather than representing a sentence as a collection of constituency-based sequences of POSs, dPUPA represents each sentence as a collection of sequences of POSs covering all identified dependency subtrees.

each dependency tree is represented as the set of all subtrees rooted by non-terminal nodes.


the percentage of tokens for which it has predicted the correct head and dependency relation.

we computed the LAS score of increasingly wider top lists of k tokens, where k ranges from 500 word tokens to the whole size of the test set (with a step size of 500 word tokens, i.e.


Since imposing a 100% LAS was too restrictive, for each experiment we defined a different α threshold taking into account the performance of each parser across the different languages and domains.

we took the top 25% and 50% of the list of ranked sentences and calculated Precision and Recall for each of them.


N-gram representations model a word type w in terms of the n-gram contexts in which w appears in a corpus.

for word w we generate the vector P (w ww )/P (w), the conditional probability of observing the word sequence w to the left and w to the right of w. The experimental section describes the particular corpora and language modeling methods used for estimating probabilities.


In this section, we evaluate our learned representations on a different task that investigates the ability of each representation to capture semantic, rather than syntactic, information.

we inves-  tigate a set-expansion task in which we're given a corpus and a few "seed" noun phrases from a semantic category (e.g.


Semi-supervised classification try to take advantage of a large amount of unlabeled data in addition to a small amount of labeled data, in order to achieve good classification accuracy while reducing the cost of manually annotating data.

graph-based techniques for semi-supervised classification (Zhou et al., 2004;Zhu et al., 2003;Callut et al., 2008;Wang et al., 2008) are recognized as a promising approach.


This latter case is the focus of this paper.

we are interested in how to construct a graph from the examples, so that the subsequent classification works well.


In this work we combine the two strategies into a single framework.

the dictionary sparsity objective of Bodrumlu et al.


However, we need additional constraints.

we need to ensure that for a given f and e exactly one variable n c f,e is 1.


However, LDAH-S yielded a much higher accuracy than LDAH-M.

this may be because the large number of training texts per author (900) may be too noisy for LDAH-M. the differences between individual texts by each author may be too large to yield a meaningful representation of the author if they are considered separately.


We then take the symmetric closure of the network, so that if (t, c) is in the network, (c, t) is, as well.

(the relation is considered undirected.)


Wiebe and Riloff (2005) used a selftrained Naïve Bayes classifier for classifying subjective sentences and achieved better recall with modest precision over several rule-based classifiers.

one shortcoming of self-training is that the resulting data may be biased: the final labeled data may consist of examples that are easiest for this particular opinion detector to identify.


We intend to fill this research gap by comparing the feasibility and effectiveness of a range of SSL approaches for opinion detection.

we aim to achieve the following goals: First, to gain a more comprehensive understanding of the utility of SSL in opinion detection.


This paper studies the problem of imposing a known hierarchical browsing structure, e.g., the electronic slides of lecture recordings, onto the sequential transcripts of the corresponding spoken document, with the aim to help index and hence access the latter more effectively.

we propose a graph-partitioning approach that optimizes a normalized-cut criterion globally, in traversing the given hierarchical semantic structures.


In this paper, we use such a sequential alignment as our baseline, which takes a standard dynamic-programming process to find the optimal path on an M-by-N similarity matrix, where M and N denote the number of bullets and utterances in a lecture, respectively.

we chose the path that maps each bullet to an utterance to achieve the highest total bullet-utterance similarity score; this path can be found within a standard O(M N 2 ) time complexity.


In addition, different form that in topic segmentation, where a segment must not be empty, we shall allow a bullet b i to be aligned to an empty region, to model the situation that a bullet is not discussed by the speaker.

to do so, we made j in equation 2and (3) above to be able to equal to k in the subscript, i.e., j ≤ k. when j = k, the set A i,j+1,k has no internal edges, and D[i, j + 1, k] is either equal to 1, or often not defined if assoc(A i,j+1,k , V ) = 0.


Spatial relationships (e.g., on, near, under) between objects are estimated.

object detectors are trained using state of the art mixtures of multi-scale deformable parts models (Felzenszwalb et al., 2010).


First, a template-based approach does not allow creative writing and produces somewhat stilted prose.

it cannot add interesting new words, or replace existing content words with better ones.


Accommodating Uncertainty We extend candidate phrase selection in order to cope with uncertainty from the image recognition.

for each object detection obj i , we include its top 3 predicted modifiers adj i1 , adj i2 , adj i3 determined by the attribute classifiers (see §2) to expand the set O 1 and O 2 accordingly.


However, our phrase fusion method is clever in that it probes reordering only on selected pairs of words, where reordering is likely to be useful.

our approach naturally ignores most word pairs that do not require reordering and has a time complexity of only O(K 2 n), where K is the maximum number of candidate phrases of any phrase type, and n is the number of phrase types in each sentence.


We instantiate ADUT for the case of Semantic Role Labeling (SRL) and show that it compares favorably with approaches that retrain their model on the target domain.

this "on the fly" adaptation approach yields 13% error reduction for a single parse system when adapting from the news wire text to fiction.


In this paper, we use the word to refer to the process of choosing the best action by considering different sequences of future actions and evaluating the structures realized by those sequences.

we introduce a lookahead mechanism that performs a search in the space of future actions.


In such cases, conventional methods rely on a multi-class classifier to determine the next operation.

a classifier is used to select the most plausible operation, by referring to the features about the current state, such as surface forms and POSs of words in the stack and the queue.


With lookahead, we choose the best action at each decision point by considering possible sequences of future actions and the states realized by those sequences.

we need to perform a search for each possible action.


Automatic learning of evaluation functions for chess programs can be seen as the training of a machine learning model.

our learning algorithm is similar to the supervised approach (Tesauro, 2001;Hoki, 2006) in that the parameters are optimized based on the differences of the feature vectors realized by the correct and incorrect actions.


Note that this update is performed only when φ(S c ) • w k − C < φ(S * ) • w k for all S c ∈ S c since otherwise S * in the learning algorithm cannot be a state with an incorrect first action.

φ(S c ) • w − φ(S * ) • w ≥ C for all S c ∈ S c after convergence.


Given these definitions, we prove the convergence for the separable case.

we assume the existence of a weight vector u (with ||u|| = 1), δ (> 0), 5 S * c and S * depend on the weight vector at each point, but we omit it from the notation for brevity.


Next, we also obtain: Combining (1) and(2), we obtain k ≤ (R 2 + 2C)/δ 2 .

the number of updates is bounded from above, meaning that the learning procedure converges after a finite number of updates.


The output of each concept node is a linear com-bination of the weights of all the terms in the original term vector.

these two layers of nodes form a complete bipartite graph as shown in Fig.


This information could be automatically deduced, but since it would add additional complexity to the already complex task, we decided to provide oracle information of this metadata both during training and testing.

speaker and author identification was not treated as an annotation layer that needed to be predicted.


Crucially, when comparing two mentions, our approach uses information from the entire clusters that contain these mentions instead of using just information local to the corresponding mentions.

mentions in a cluster share their attributes (e.g., number, gender, animacy) between them so coreference decision are better informed.


Our results on the development set suggest that a linguistically-rich system for coreference resolution might benefit a lot from feature selection.

we have investigated Non-Dominated Sorting Genetic Algorithm II (Deb et al., 2002) for multiobjective optimization.


Coreference resolution, defined as finding the different mentions in a document which refer to the same entity in reality, is an important subject in Natural Language Processing.

coreference resolution is a critical component of information extraction systems (Chinchor and Nancy, 1998;Sundheim and Beth, 1995) and a series of coreference resolution tasks have been introduced and evaluated from MUC (MUC-6, 1995).


Noun phrase coreference resolution is the process of finding markables (noun phrase) referring to the same real world entity or concept.

this process groups the markables of a document into entities (equivalence classes) so that all markables in an entity are coreferent.


Since our focus when extending our system for this task was on mention detection, we present results with variations in the sets of mention filters used.

we have included results for our baseline system (None), when only the filters before coreference resolution are used (Pre), when only the filters after coreference resolution are used (Post), and when all filters are used (All).


The main approach behind the pre-coreference filters was to consider the parse to catch cases that are almost never mentions.

these filters target cases that are explicitly excluded by the annotation scheme.


Most of the filters constructed in this way catch some true mentions as well as spurious mentions, leading to significant improvements in precision at the cost of recall.

an increase of 21.46% in precision and decrease of 8.85% in recall, but an overall increase of 12.74% in F 1 -score.


In our algorithm, we only detect the closest antecedent for each entity, instead of all coreferences, of each entity.

we define each training and testing instance as a pair of entities.


We use Reconcile's single-link clustering algorithm.

we compute the transitive closure of the positive pairwise predictions.


We slightly modify the conjunction scheme and expand it with additional backed-off conjunctions, since these changes lead to features that empirically transfer better than the MSTParser defaults.

we use conjunctions with attachment direction (left or right), coarsened distance, 4 and attachment direction and coarsened distance combined.


We argued in Section 3.4 that the cross-lingual transfer step of our method imposes a fundamental limitation on how useful any such approach can be, which we now investigate further.

any syntactic divergence, especially inconsistent divergences like head switching, will limit the utility of transferred structure.


During the training stage, for each language, we find mappings (or projection directions) and U i ∈ R (c×k) from the character and phonemic spaces into a k-dimensional subspace (or an interlingual representation) such that a name gets mapped to the same k-dimensional vector irrespective of the language.

given a name x i it gets mapped to the vector A T i x i and similarly its IPA sequence p i gets mapped to U T i p i .


The fourth and fifth rows of Table 4 also show the multilingual results.

we train our system on data from the three languages En, Bg, and Ru and test it on En-Bg and En-Ru test sets.


For example, the English word "piranha" has the seed distribution where the weights of the seeds "Amazon", "jungle", and "fish" are 0.5, 0.3, and 0.2, respectively.

each of seed distributions is updated as follows: where is the set of vertices linking to v i .


We do this by fitting a replacement to m * : m ≤ m * , based on the classifier's training data, such that too-large elements will be accommodated into the stream by implicitly assuming that the portion of a value that falls outside the restricted window is "spread out" over the previously observed values.

we modify the contents of the implicit reservoir by rewriting history: pretending that earlier elements were larger than they were, but still within the reduced window.


Our aim is to model the user, and are interested in the conditional distribution of the user utterances given the dialog up to that point.

we are interested in the distribution p where d n is either a machine utterance m n or a user utterance u n .


In this paper, we present a novel approach to incremental decision making for output planning that is based on Hierarchical Reinforcement Learning (HRL).

we address the problem of optimising IP strategies while allowing the system to generate and comprehend backchannels and bargeins based on a partially data-driven reward function.


The agent learns to barge-in or generate backchannels to users at points where the ID is low but rising.

the agent learns to barge-in right before information density peaks in an incoming user utterance to clarify or request slots that are still open from the previous information density peak.


While we introduce a general model, we will focus on the task of unsupervised conversation modeling.

we build off the Bayesian block HMMs used by Ritter et al.


We also compare against LDA, which makes latent assignments at the token-level, but blocks of text are independent of each other.

bHMM models sequential dependencies but allows only single-class membership, whereas LDA uses no sequence information but has a mixed membership property.


Given two sets of variable assignments z and z , the variation of information is defined as H(Z|Z ) + H(Z |Z).

given one clustering, how much uncertainty do we have about the other?


The main drawback of these hybrid methods, however, is that they model the context compatibility and the topic coherence separately, which makes it difficult to capture the mutual reinforcement effect between the above two directions.

the topic coherence and the context compatibility are highly correlated and their evidence can be used to reinforce each other in EL decisions.


In this paper, we propose a method to jointly model and exploit the context compatibility, the topic coherence and the correlation between them for better EL performance.

we propose a generative probabilistic model -called entity-topic model, which can uniformly model the text compatibility and the topic coherence as the statistical dependencies between the mentions, the words, the underlying entities and the underlying topics of a document by assuming that each document is generated according to the following two assumptions: 1) Topic coherence assumption: All entities in a document should be centered around the main topics of the document.


Entity Name Knowledge Ã Ã (The name distribution of entities): In our model, all name mentions are generated using the name knowledge of its referent entity.

we model the name knowledge of an entity as a multinomial distribution of its names, with the probability indicating the likelihood this entity is mentioned by the name.


In this paper we estimate the global knowledge through Bayesian inference by also incorporating the knowledge generation process into our model.

given the topic number T, the entity number E, the name number K and the word number V, the entity-topic model generates the global knowledge as follows: 1) Áj¯» Dir (¯) Áj¯» Dir(¯) For each topic z, our model samples its entity distribution Á z Á z from an E-dimensional Dirichlet distribution with hyperparameter ¯.


For this reason, we propose an approximate inference algorithmthe Gibbs sampling algorithm for the entity-topic model by extending the well-known Gibbs sampling algorithm for LDA (Griffiths & Steyvers, 2004).

in Gibbs sampling, we first construct the posterior distribution P (z; e; ajD) P (z; e; ajD) , then this posterior distribution is used to: 1) estimate μ μ, Á Á, Ã Ã and » »; and 2) predict the entities and the topics of all documents in D. we first derive the joint posterior distribution from Eq.


However, because the model needs to be able to transfer to arbitrary new domains, the domain adaptation model is restricted to domain-independent features, which are "coarsergrained."

the distant supervision model has the ability to place more weight on attributes like director rather than genre, or team rather than position, if those attributes are more discriminative.


In this paper, we present initial work towards facilitating the process of building a generic NER extractor using induction techniques.

given as input an annotated document corpus, a set of BF rules, and a default CO rule for each entity type, our goal is to generate a set of CD and CR rules such that the resulting extractor constitutes a good starting point for further refinement by a developer.


To this end, we define a simple complexity score that is a function of the number of rules, and the number of input views to each rule of the extractor.

we define the length of rule R, denoted as L(R), as the number of input views in the from clause(s) of the view.


As we increase the number of BFs, the accuracies of the induced extractors increases, at the cost of an increase in complexity.

the recall increases significantly across the board, and is more prominent between E2 and E3, where the additional domain specific features result in recall increase from 5.9% to 47.5% for ORG.


Recall that our framework is limited to a small subset of AQL constructs for expressing CD and CR rules, and there is a single consolidation rule.

advanced constructs such as dynamic dictionaries are not supported, and the set of predicates to the Filter construct supported in our system is restricted to predicates over other concepts, which is only a subset of those used in (Chiticariu et al., 2010b).


To address these challenges, we propose a novel active learning approach, named co-selecting, by taking both the imbalanced class distribution issue and uncertainty into account.

our co-selecting approach employs two feature subspace classifiers to collectively select most informative minority-class samples for manual annotation by leveraging a certainty measurement and an uncertainty measurement, and in the meanwhile, automatically label most informative majority-class samples, to reduce humanannotation efforts.


The basic idea of our active learning approach is to use two complementary classifiers for collectively selecting most informative MI samples: one to adopt a certainty measurement for selecting most possible MI samples and the other to adopt an uncertainty measurement for selecting most uncertain MI samples from the most possible MI samples returned from the first classifier.

the two classifiers are trained with two disjoint feature subspaces to guarantee their complementariness.


Figure 4 compares different active learning approaches to imbalanced sentiment classification when 600 unlabeled samples are selected for annotation.

the parameters  and k is set to be 1/16 and 50 respectively.


This verifies the effectiveness of automatically labeling those selected MA samples in imbalanced sentiment classification.

we notice that only using the certainty measurement (i.e., certainty) performs worst, which reflects that only considering sample balance factor in imbalanced sentiment classification is not helpful.


Heuristic patterns can also serve as base predictors.

we found that some authors list positive and negative aspects separately after keywords such as "pros" and "cons".


Thus sentence ratings should be close to document ratings unless strong evidence to the contrary exists.


When no manually created sentence-level polarity labels are available, we set the value of η Doc i depending on the polarity class.

we set η Doc i = 1 for both positive and negative documents, and η Doc i = 2 for neutral documents.


If so, we set the corresponding variance η Doc i to a small value; otherwise, we choose a larger value.

we train a logistic regression classifier (Bishop, 2006) using the following binary features: (1) an indicator variable for each document polarity, and (2) an indicator variable for each triple of base predictor, predicted polarity, and document polarity (set to 1 if the polarities match).


To address data sparsity, we incorporate synonyms and hypernyms from WordNet into our kernel.

we represent words found in WordNet by their first two synset names (for verbs, adjectives, nouns) and their direct hypernym (nouns only).


In this section, we first define a graph representation for pairs of documents.

predicates are represented as nodes in such a graph and similarities between predicates as edges.


simultaneously, we should also focus on each type of argument independently.

we apply this approach to compute similarity between verbs based on the set of adverbs that modify them.


We follow prior work on supervised lexical semantics (Mirkin et al., 2006;Hagiwara et al., 2009;Tremper, 2010) and address the rule learning task as a classification task.

given an ordered verb pair (v 1 , v 2 ) as input, we learn a classifier that detects whether the entailment relation 'v 1 → v 2 ' holds for this pair.


Still, they do complement each of the other feature groups.

since the Sent+Doc-level model performs almost as good as the full model, this subset may be a good substitute to the full model, since its features are easier to extract from large corpora, as they may be extracted in an on-line fashion, processing one document at a time (contrary to corpus-level features).


(2008) to latent variable dependency trees like us but under the restrictive conditions that model parameters are trained for a specified, albeit arbitrary, tree topology.

2 all training sentences and test sentences must have identical tree topologies.


Define T d and T u d where d ∈ {L, R} to be the hidden state transition matrices from parent to left or right child, and from left or right child to parent (hence the u for 'up'), respectively.

(referring to Figure 2) Let U x(i) be the i th entry of vector U δ x and G = U O.


Define Λ as the smallest element of µ, Σ −1 , Ω −1 , and K().

where K ijk = K(δ j ) ik are the elements of the tensor K().


More formally, let s(i) be the maximal set of continuous indices j ≥ i including i such that, if j = i, c j = 0.

s(i) are the indices comprising the remainder of the phrase beginning at position i.


Let c k be a counter indicating the number of times ϕ k has been selected.

we set r n to ϕ k with probability and increment c k , or with probability we draw a new rule from G 0 , append it to ϕ, and use it for r n .


We adopt the synchronous tree substitution grammar (STSG) formalism (Eisner, 2003) which can model non-isomorphic tree structures (the grammar rules can comprise trees of arbitrary depth) and is thus suited to text-rewriting tasks which typically involve a number of local modifications to the input text.

we propose quasi-synchronous tree substitution grammar (QTSG) as a flexible formalism to learn general treeedits from loosely-aligned phrase structure trees.


We decided to learn which of these elements (represented as nodes in the parse tree) are informative from training data.

sentences in the cluster documents were aligned to sentences from corresponding human summaries.


3 Note that although the differences in BLEU scores are small, they end up being statistically significant because the models frequently yield the same top scoring realization, and reliably deliver improvements in the cases where they differ.

note that DEPLEN and DEPORD-NF agree on the best realization 81% of the time, while DEPLEN-NODIST and DEPORD-NODIST have 78.1% agreement, and DEPLEN-GLOBAL and GLOBAL show 77.4% agreement; by comparison, DEPORD-NODIST and GLOBAL only agree on the best realization 51.1% of the time.


Intriguingly, there is some evidence that a negatively weighted total dependency length feature can go too far in minimizing dependency length, in the absence of other informative features to counterbalance it.

the DEPLEN-GLOBAL model in Table 8 has significantly lower dependency length than the corpus, but in the richer models with discriminative synactic and dependency ordering features, there are no significant differences.


To determine whether heavy-light ordering differences often represent ordering errors (including egregious ones), rather than simply representing acceptable variation, we conducted a targeted human evaluation on examples of this kind.

for each of the DEPLEN* models and their corresponding models without the dependency length feature, we chose the 25 sentences from the development section whose realizations exhibited the greatest difference in dependency length between sibling constituents appearing in opposite orders, and asked two judges (not the authors) to choose which of the two realizations best expressed the meaning of the reference sentence in a grammatical and fluent way, with the choice forced (2AFC).


As a representative, Lapata and Lascarides (2006) proposed a simple yet effective data-intensive approach.

they trained models on main and subordinate clauses connected with some special temporal marker words, such as "after" and "before", and employed them in temporal inference.


(2011) proposed a parallel mapping method to automatically generate annotated data.

they used English verbs to label tense information for Chinese verbs via a parallel Chinese-English corpus.


(2001) relied on LCS to generate appropriately-tensed English translations for Chinese.

they addressed tense reconstruction on a binary taxonomy (present and past) for Chinese text and reported that incorporating lexical aspect features of telicity can obtain a 20% to 35% boost in accuracy on tense realization.


This paper explores document-level SMT from the tense perspective.

we focus on how to build document-level and sentence-level tense models and how to integrate such models into a popular SMT system.


Among the advantages of transition-based parsers are the linear time complexity of many of them and the possibility of using rich feature models (Zhang and Nivre, 2011).

many transition-based parsers (Nivre et al., 2004;Attardi, 2006;Sagae and Tsujii, 2008;Nivre, 2009;Huang and Sagae, 2010;Gómez-Rodríguez and Nivre, 2010) are stack-based (Nivre, 2008), meaning that they keep a stack of partially processed tokens and an input buffer of unread tokens.


Here the dashed arrows indicate additional information stored in each chart-cell.

the previous modifier in complete chart items.


Next, we looked at the impact of our system on non-English treebanks.


We again see that approximate decoding with rich features and cube pruning has higher accuracy than transition-based parsing with a large beam.

for the ZH-CTB data set, our system is currently the best reported result.


(iii) Finally, an analysis of our grapheme-phoneme predictions shows that they do not achieve certain global characteristics observed across true phoneme inventories.

the level of "feature economy" in our predictions is too low, suggesting an avenue for future research.


different kinds of voicing, manner, and place) that are used to distinguish consonant phonemes from one another (Clements, 2003).

once an articulatory feature is used to mark off one phoneme from another, it will likely be used again to differentiate other phoneme pairs in the same language.


It seems that Phonemic Context Features: A perfect featureset would depend on the entire set of graphemeto-phoneme predictions for a language.

we would ideally map all the graphemes in our text to phonemes, and then consider the plausibility of the resulting phoneme sequences.


Similarly, John Jacob Jingelhimer Smith and Dr. J. J. Jingleheimer may also be related names for this person.

these names may be derived from one another, via unseen relationships, although we cannot be sure.


Our EM learning procedure found p jointly with the phylogeny.

it alternated between improving p and improving the distribution over phylogenies.


From this characterization of the CRP, one can see that any permutation of this entity sequence would have the same probability.

our distribution over sequences of entities e is exchangeable.


Our phylogeny is a preferential attachment tree, a random directed graph in which each vertex selects a single previous vertex as its parent.

it is a random recursive tree (Smythe and Mahmoud, 1995) whose vertices are the tokens.


Suppose we were lucky enough to fully observe the sequence of named entity tokens y i = (e i , w i ) produced by our generative model.

suppose all tokens were tagged and we knew their ordering.


This result has previously been employed in nonprojective dependency parsing (Koo et al., 2007;Smith and Smith, 2007).

let l ∈ R n×n denote the laplacian of G, namely Tutte's theorem relates the determinant of the laplacian to the spanning trees in graph G. the cofactor l 0,0 equals the total weight of all directed spanning trees rooted at node 0.


We used Wikipedia to create a list of name aliases for different entities.

we mined English Wikipedia 11 for all redirects: page names that lead directly to another page.


sentence/phrase boundaries, neighboring pauses and punctuation) For each word in the AMI corpus, we obtained two alternative estimates of word duration: one version which is independent of a word's sentential context, and a second version which does take into account the sentential context (such as phrasal/sentential and across-word-boundaries phonological features) the word occurs in.

we obtain MARY word duration estimates in the second version by running individual whole sentences through MARY, segmented by standard punctuation marks used in the AMI corpus transcriptions.


This way, we could obtain insights into our building blocks, in addition to assessing the end-to-end performance.

we could assess the goodness of the question-to-query translation independently of the actual answer quality which may depend on particularities of the underlying datasets (e.g., slight mismatches between query terminology and the names in the data.)


We develop a new framework for opinion Questions Answering, which enables accurate question analysis and effective answer generation by making use the hierarchy.

we first identify the (explicit/implicit) product aspects asked in the questions and their sub-aspects by referring to the hierarchy.


As shown in Figure 2, the hierarchy organizes product aspects as nodes, following their parent-child relations.

this method first automatically acquires an initial aspect hierarchy from the domain knowledge and identifies aspects commented in the reviews.


Recognizing the product: A name entity recognizer 1 is trained to recognize the product name.

we collect 420 auxiliary questions from Yahoo!Answer 2 , and manually annotate the product names (submitted as supplementary material in Appendix A).


The technique simultaneously learns to identify explicit aspects, and discovers the associations between aspects and sentiment terms by multiple classifiers.

given a testing question, we identify its aspect by hierarchically classify (Silla et al., 2011) it into the appropriate aspect node of a particular product hierarchy.


Subsequently, we learn a SVM sentiment classifier to determine the opinion polarity of the opinion questions.

the reviews and corresponding opinions stored in the hierarchy are used as training samples, which are represented by the unigram features.